Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations1818
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory411.9 KiB
Average record size in memory232.0 B

Variable types

Numeric9
Categorical19

Alerts

zprior has constant value "1" Constant
age is highly overall correlated with age_catHigh correlation
age_cat is highly overall correlated with ageHigh correlation
cd40 is highly overall correlated with cd420High correlation
cd420 is highly overall correlated with cd40High correlation
cd80 is highly overall correlated with cd820High correlation
cd820 is highly overall correlated with cd80High correlation
cid is highly overall correlated with timeHigh correlation
gender is highly overall correlated with homo and 1 other fieldsHigh correlation
hemo is highly overall correlated with pidnumHigh correlation
homo is highly overall correlated with gender and 1 other fieldsHigh correlation
homo_cat is highly overall correlated with gender and 1 other fieldsHigh correlation
pidnum is highly overall correlated with hemoHigh correlation
preanti is highly overall correlated with str2 and 2 other fieldsHigh correlation
race is highly overall correlated with race_catHigh correlation
race_cat is highly overall correlated with raceHigh correlation
str2 is highly overall correlated with preanti and 2 other fieldsHigh correlation
strat is highly overall correlated with preanti and 2 other fieldsHigh correlation
time is highly overall correlated with cidHigh correlation
treat is highly overall correlated with trtHigh correlation
trt is highly overall correlated with treatHigh correlation
z30 is highly overall correlated with preanti and 2 other fieldsHigh correlation
hemo is highly imbalanced (58.5%) Imbalance
oprior is highly imbalanced (84.4%) Imbalance
pidnum has unique values Unique
preanti has 743 (40.9%) zeros Zeros

Reproduction

Analysis started2024-12-19 11:05:34.505668
Analysis finished2024-12-19 11:06:01.560488
Duration27.05 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

pidnum
Real number (ℝ)

High correlation  Unique 

Distinct1818
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245983.67
Minimum10056
Maximum990030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:01.755180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10056
5-th percentile11435.85
Q181448.25
median190542.5
Q3278104.25
95-th percentile910057.3
Maximum990030
Range979974
Interquartile range (IQR)196656

Descriptive statistics

Standard deviation230057.93
Coefficient of variation (CV)0.93525693
Kurtosis2.7833841
Mean245983.67
Median Absolute Deviation (MAD)109086.5
Skewness1.7566055
Sum4.4719832 × 108
Variance5.2926653 × 1010
MonotonicityStrictly increasing
2024-12-19T11:06:02.150515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10056 1
 
0.1%
250465 1
 
0.1%
251039 1
 
0.1%
251038 1
 
0.1%
251033 1
 
0.1%
251032 1
 
0.1%
251029 1
 
0.1%
251028 1
 
0.1%
251027 1
 
0.1%
251026 1
 
0.1%
Other values (1808) 1808
99.4%
ValueCountFrequency (%)
10056 1
0.1%
10093 1
0.1%
10124 1
0.1%
10140 1
0.1%
10165 1
0.1%
10190 1
0.1%
10198 1
0.1%
10229 1
0.1%
10241 1
0.1%
10341 1
0.1%
ValueCountFrequency (%)
990030 1
0.1%
990026 1
0.1%
990021 1
0.1%
990019 1
0.1%
980046 1
0.1%
980045 1
0.1%
980042 1
0.1%
980041 1
0.1%
980039 1
0.1%
980038 1
0.1%

cid
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1399 
1
419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1399
77.0%
1 419
 
23.0%

Length

2024-12-19T11:06:02.432388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:02.673204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1399
77.0%
1 419
 
23.0%

Most occurring characters

ValueCountFrequency (%)
0 1399
77.0%
1 419
 
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1399
77.0%
1 419
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1399
77.0%
1 419
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1399
77.0%
1 419
 
23.0%

time
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean898.86579
Minimum182
Maximum1231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:03.281263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum182
5-th percentile311.7
Q1772.75
median1002
Q31091
95-th percentile1160
Maximum1231
Range1049
Interquartile range (IQR)318.25

Descriptive statistics

Standard deviation269.7838
Coefficient of variation (CV)0.30013802
Kurtosis0.080936529
Mean898.86579
Median Absolute Deviation (MAD)110
Skewness-1.1433471
Sum1634138
Variance72783.297
MonotonicityNot monotonic
2024-12-19T11:06:03.623217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1087 30
 
1.7%
1088 21
 
1.2%
1154 17
 
0.9%
1091 16
 
0.9%
1089 13
 
0.7%
993 13
 
0.7%
1014 12
 
0.7%
1097 12
 
0.7%
1090 12
 
0.7%
896 11
 
0.6%
Other values (627) 1661
91.4%
ValueCountFrequency (%)
182 2
0.1%
184 1
0.1%
188 1
0.1%
189 1
0.1%
197 2
0.1%
198 1
0.1%
204 1
0.1%
208 1
0.1%
210 1
0.1%
211 2
0.1%
ValueCountFrequency (%)
1231 3
0.2%
1230 1
 
0.1%
1224 4
0.2%
1223 1
 
0.1%
1217 1
 
0.1%
1214 2
0.1%
1211 1
 
0.1%
1209 2
0.1%
1206 1
 
0.1%
1203 3
0.2%

trt
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
3
484 
2
458 
0
440 
1
436 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
3 484
26.6%
2 458
25.2%
0 440
24.2%
1 436
24.0%

Length

2024-12-19T11:06:03.916349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:04.152074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 484
26.6%
2 458
25.2%
0 440
24.2%
1 436
24.0%

Most occurring characters

ValueCountFrequency (%)
3 484
26.6%
2 458
25.2%
0 440
24.2%
1 436
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 484
26.6%
2 458
25.2%
0 440
24.2%
1 436
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 484
26.6%
2 458
25.2%
0 440
24.2%
1 436
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 484
26.6%
2 458
25.2%
0 440
24.2%
1 436
24.0%

age
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.561056
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:04.420982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile23
Q129
median34
Q340
95-th percentile48
Maximum56
Range43
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.7219755
Coefficient of variation (CV)0.22342996
Kurtosis-0.13430496
Mean34.561056
Median Absolute Deviation (MAD)5
Skewness0.24245225
Sum62832
Variance59.628906
MonotonicityNot monotonic
2024-12-19T11:06:04.715035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
33 97
 
5.3%
29 96
 
5.3%
32 96
 
5.3%
31 94
 
5.2%
35 92
 
5.1%
37 89
 
4.9%
27 88
 
4.8%
30 84
 
4.6%
28 80
 
4.4%
34 79
 
4.3%
Other values (34) 923
50.8%
ValueCountFrequency (%)
13 2
 
0.1%
14 4
 
0.2%
15 3
 
0.2%
16 6
 
0.3%
17 4
 
0.2%
18 6
 
0.3%
19 6
 
0.3%
20 16
0.9%
21 16
0.9%
22 20
1.1%
ValueCountFrequency (%)
56 5
 
0.3%
55 7
 
0.4%
54 9
 
0.5%
53 6
 
0.3%
52 11
 
0.6%
51 9
 
0.5%
50 16
0.9%
49 20
1.1%
48 30
1.7%
47 29
1.6%

wtkg
Real number (ℝ)

Distinct579
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.229285
Minimum43.00128
Maximum105.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:05.015792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum43.00128
5-th percentile55.6799
Q166.6792
median73.8
Q381.648
95-th percentile94.13
Maximum105.9
Range62.89872
Interquartile range (IQR)14.9688

Descriptive statistics

Standard deviation11.468046
Coefficient of variation (CV)0.1544949
Kurtosis-0.06945605
Mean74.229285
Median Absolute Deviation (MAD)7.5
Skewness0.12667857
Sum134948.84
Variance131.51608
MonotonicityNot monotonic
2024-12-19T11:06:05.322016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.7616 24
 
1.3%
77.112 22
 
1.2%
78.0192 21
 
1.2%
72.576 18
 
1.0%
73.0296 17
 
0.9%
69.8544 17
 
0.9%
76.2048 16
 
0.9%
66.6792 16
 
0.9%
69.4008 16
 
0.9%
86.184 16
 
0.9%
Other values (569) 1635
89.9%
ValueCountFrequency (%)
43.00128 1
0.1%
43.8 1
0.1%
44.18064 1
0.1%
44.226 1
0.1%
45 1
0.1%
45.36 1
0.1%
45.4 1
0.1%
45.6 1
0.1%
45.8136 2
0.1%
46.8 2
0.1%
ValueCountFrequency (%)
105.9 1
 
0.1%
105.6888 2
0.1%
105.0084 1
 
0.1%
104.5 1
 
0.1%
104 2
0.1%
103.6 1
 
0.1%
103.5 1
 
0.1%
103.4208 3
0.2%
103.2 1
 
0.1%
103.194 1
 
0.1%

hemo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1666 
1
 
152

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1666
91.6%
1 152
 
8.4%

Length

2024-12-19T11:06:05.604409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:05.934672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1666
91.6%
1 152
 
8.4%

Most occurring characters

ValueCountFrequency (%)
0 1666
91.6%
1 152
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1666
91.6%
1 152
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1666
91.6%
1 152
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1666
91.6%
1 152
 
8.4%

homo
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
1205 
0
613 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1205
66.3%
0 613
33.7%

Length

2024-12-19T11:06:06.310993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:06.690113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1205
66.3%
0 613
33.7%

Most occurring characters

ValueCountFrequency (%)
1 1205
66.3%
0 613
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1205
66.3%
0 613
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1205
66.3%
0 613
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1205
66.3%
0 613
33.7%

drugs
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1586 
1
232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1586
87.2%
1 232
 
12.8%

Length

2024-12-19T11:06:07.114262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:07.462333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1586
87.2%
1 232
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0 1586
87.2%
1 232
 
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1586
87.2%
1 232
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1586
87.2%
1 232
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1586
87.2%
1 232
 
12.8%

karnof
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
100
1099 
90
657 
80
 
62

Length

Max length3
Median length3
Mean length2.6045105
Min length2

Characters and Unicode

Total characters4735
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 1099
60.5%
90 657
36.1%
80 62
 
3.4%

Length

2024-12-19T11:06:07.879486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:08.650399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
100 1099
60.5%
90 657
36.1%
80 62
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 2917
61.6%
1 1099
 
23.2%
9 657
 
13.9%
8 62
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2917
61.6%
1 1099
 
23.2%
9 657
 
13.9%
8 62
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2917
61.6%
1 1099
 
23.2%
9 657
 
13.9%
8 62
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2917
61.6%
1 1099
 
23.2%
9 657
 
13.9%
8 62
 
1.3%

oprior
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1777 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1777
97.7%
1 41
 
2.3%

Length

2024-12-19T11:06:09.323305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:10.108598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1777
97.7%
1 41
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1777
97.7%
1 41
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1777
97.7%
1 41
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1777
97.7%
1 41
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1777
97.7%
1 41
 
2.3%

z30
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
995 
0
823 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 995
54.7%
0 823
45.3%

Length

2024-12-19T11:06:10.512490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:10.942932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 995
54.7%
0 823
45.3%

Most occurring characters

ValueCountFrequency (%)
1 995
54.7%
0 823
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 995
54.7%
0 823
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 995
54.7%
0 823
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 995
54.7%
0 823
45.3%

zprior
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
1818 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1818
100.0%

Length

2024-12-19T11:06:11.271122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:11.477376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1818
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1818
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1818
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1818
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1818
100.0%

preanti
Real number (ℝ)

High correlation  Zeros 

Distinct718
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean367.88339
Minimum0
Maximum1826
Zeros743
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:11.718911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median152.5
Q3723
95-th percentile1241.5
Maximum1826
Range1826
Interquartile range (IQR)723

Descriptive statistics

Standard deviation444.66622
Coefficient of variation (CV)1.2087151
Kurtosis0.074937271
Mean367.88339
Median Absolute Deviation (MAD)152.5
Skewness1.0312205
Sum668812
Variance197728.05
MonotonicityNot monotonic
2024-12-19T11:06:12.061661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 743
40.9%
917 6
 
0.3%
175 5
 
0.3%
238 5
 
0.3%
768 5
 
0.3%
213 5
 
0.3%
832 4
 
0.2%
7 4
 
0.2%
807 4
 
0.2%
925 4
 
0.2%
Other values (708) 1033
56.8%
ValueCountFrequency (%)
0 743
40.9%
2 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 3
 
0.2%
7 4
 
0.2%
10 1
 
0.1%
13 1
 
0.1%
15 2
 
0.1%
16 1
 
0.1%
ValueCountFrequency (%)
1826 1
0.1%
1775 1
0.1%
1774 1
0.1%
1759 1
0.1%
1756 1
0.1%
1755 1
0.1%
1737 1
0.1%
1729 1
0.1%
1725 1
0.1%
1673 1
0.1%

race
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1289 
1
529 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1289
70.9%
1 529
29.1%

Length

2024-12-19T11:06:12.354633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:12.573351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1289
70.9%
1 529
29.1%

Most occurring characters

ValueCountFrequency (%)
0 1289
70.9%
1 529
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1289
70.9%
1 529
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1289
70.9%
1 529
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1289
70.9%
1 529
29.1%

gender
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
1500 
0
318 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1500
82.5%
0 318
 
17.5%

Length

2024-12-19T11:06:12.807145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:13.026785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1500
82.5%
0 318
 
17.5%

Most occurring characters

ValueCountFrequency (%)
1 1500
82.5%
0 318
 
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1500
82.5%
0 318
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1500
82.5%
0 318
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1500
82.5%
0 318
 
17.5%

str2
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
1065 
0
753 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1065
58.6%
0 753
41.4%

Length

2024-12-19T11:06:13.270470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:13.483087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1065
58.6%
0 753
41.4%

Most occurring characters

ValueCountFrequency (%)
1 1065
58.6%
0 753
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1065
58.6%
0 753
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1065
58.6%
0 753
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1065
58.6%
0 753
41.4%

strat
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
753 
3
713 
2
352 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 753
41.4%
3 713
39.2%
2 352
19.4%

Length

2024-12-19T11:06:13.709621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:13.925736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 753
41.4%
3 713
39.2%
2 352
19.4%

Most occurring characters

ValueCountFrequency (%)
1 753
41.4%
3 713
39.2%
2 352
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 753
41.4%
3 713
39.2%
2 352
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 753
41.4%
3 713
39.2%
2 352
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 753
41.4%
3 713
39.2%
2 352
19.4%

symptom
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1503 
1
315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1503
82.7%
1 315
 
17.3%

Length

2024-12-19T11:06:14.198251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:14.410232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1503
82.7%
1 315
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0 1503
82.7%
1 315
 
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1503
82.7%
1 315
 
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1503
82.7%
1 315
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1503
82.7%
1 315
 
17.3%

treat
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
1378 
0
440 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1378
75.8%
0 440
 
24.2%

Length

2024-12-19T11:06:14.641773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:14.858226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1378
75.8%
0 440
 
24.2%

Most occurring characters

ValueCountFrequency (%)
1 1378
75.8%
0 440
 
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1378
75.8%
0 440
 
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1378
75.8%
0 440
 
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1378
75.8%
0 440
 
24.2%

offtrt
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1181 
1
637 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1181
65.0%
1 637
35.0%

Length

2024-12-19T11:06:15.102206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:15.337076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1181
65.0%
1 637
35.0%

Most occurring characters

ValueCountFrequency (%)
0 1181
65.0%
1 637
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1181
65.0%
1 637
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1181
65.0%
1 637
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1181
65.0%
1 637
35.0%

cd40
Real number (ℝ)

High correlation 

Distinct438
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345.30968
Minimum70
Maximum663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:15.581957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile184
Q1263
median340
Q3420
95-th percentile530
Maximum663
Range593
Interquartile range (IQR)157

Descriptive statistics

Standard deviation107.5161
Coefficient of variation (CV)0.31136137
Kurtosis-0.30085904
Mean345.30968
Median Absolute Deviation (MAD)80
Skewness0.32934597
Sum627773
Variance11559.711
MonotonicityNot monotonic
2024-12-19T11:06:15.879800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420 23
 
1.3%
300 23
 
1.3%
280 21
 
1.2%
410 18
 
1.0%
380 16
 
0.9%
400 16
 
0.9%
320 16
 
0.9%
230 15
 
0.8%
390 15
 
0.8%
310 15
 
0.8%
Other values (428) 1640
90.2%
ValueCountFrequency (%)
70 1
0.1%
84 1
0.1%
99 1
0.1%
103 1
0.1%
110 1
0.1%
112 1
0.1%
120 1
0.1%
122 1
0.1%
123 1
0.1%
124 1
0.1%
ValueCountFrequency (%)
663 1
0.1%
659 1
0.1%
658 1
0.1%
653 1
0.1%
650 1
0.1%
648 1
0.1%
647 1
0.1%
646 2
0.1%
645 1
0.1%
643 1
0.1%

cd420
Real number (ℝ)

High correlation 

Distinct511
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366.76458
Minimum74
Maximum744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:16.172360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile173.85
Q1270.25
median352
Q3456
95-th percentile597
Maximum744
Range670
Interquartile range (IQR)185.75

Descriptive statistics

Standard deviation129.63449
Coefficient of variation (CV)0.35345422
Kurtosis-0.32477718
Mean366.76458
Median Absolute Deviation (MAD)89
Skewness0.38227986
Sum666778
Variance16805.1
MonotonicityNot monotonic
2024-12-19T11:06:16.492552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 22
 
1.2%
390 16
 
0.9%
270 15
 
0.8%
380 14
 
0.8%
400 13
 
0.7%
290 13
 
0.7%
320 12
 
0.7%
450 12
 
0.7%
360 12
 
0.7%
510 12
 
0.7%
Other values (501) 1677
92.2%
ValueCountFrequency (%)
74 1
0.1%
80 2
0.1%
81 1
0.1%
88 1
0.1%
90 2
0.1%
96 1
0.1%
98 1
0.1%
101 1
0.1%
107 1
0.1%
109 2
0.1%
ValueCountFrequency (%)
744 1
 
0.1%
729 1
 
0.1%
721 1
 
0.1%
720 2
0.1%
715 1
 
0.1%
713 2
0.1%
710 1
 
0.1%
709 1
 
0.1%
707 2
0.1%
706 3
0.2%

cd80
Real number (ℝ)

High correlation 

Distinct938
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean912.39219
Minimum105
Maximum1984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:16.795890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile407.7
Q1640
median867.5
Q31134
95-th percentile1592.6
Maximum1984
Range1879
Interquartile range (IQR)494

Descriptive statistics

Standard deviation357.87543
Coefficient of variation (CV)0.39223859
Kurtosis-0.1170462
Mean912.39219
Median Absolute Deviation (MAD)245.5
Skewness0.56199962
Sum1658729
Variance128074.82
MonotonicityNot monotonic
2024-12-19T11:06:17.546425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
870 9
 
0.5%
950 9
 
0.5%
1000 9
 
0.5%
620 8
 
0.4%
1040 8
 
0.4%
880 8
 
0.4%
680 8
 
0.4%
850 7
 
0.4%
570 7
 
0.4%
990 7
 
0.4%
Other values (928) 1738
95.6%
ValueCountFrequency (%)
105 1
0.1%
116 1
0.1%
137 1
0.1%
177 1
0.1%
207 1
0.1%
218 1
0.1%
221 1
0.1%
225 2
0.1%
228 1
0.1%
230 1
0.1%
ValueCountFrequency (%)
1984 1
0.1%
1970 1
0.1%
1964 2
0.1%
1962 1
0.1%
1950 1
0.1%
1949 1
0.1%
1943 1
0.1%
1929 1
0.1%
1920 2
0.1%
1914 1
0.1%

cd820
Real number (ℝ)

High correlation 

Distinct899
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean865.9879
Minimum131
Maximum1809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2024-12-19T11:06:17.864033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum131
5-th percentile396
Q1620
median828.5
Q31083
95-th percentile1463.05
Maximum1809
Range1678
Interquartile range (IQR)463

Descriptive statistics

Standard deviation323.62032
Coefficient of variation (CV)0.37370074
Kurtosis-0.22251415
Mean865.9879
Median Absolute Deviation (MAD)228.5
Skewness0.47131621
Sum1574366
Variance104730.11
MonotonicityNot monotonic
2024-12-19T11:06:18.170935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
920 11
 
0.6%
730 10
 
0.6%
710 10
 
0.6%
530 9
 
0.5%
700 9
 
0.5%
740 9
 
0.5%
570 8
 
0.4%
720 8
 
0.4%
590 8
 
0.4%
620 8
 
0.4%
Other values (889) 1728
95.0%
ValueCountFrequency (%)
131 1
0.1%
140 1
0.1%
173 1
0.1%
195 1
0.1%
200 1
0.1%
213 1
0.1%
214 1
0.1%
218 1
0.1%
220 2
0.1%
224 1
0.1%
ValueCountFrequency (%)
1809 1
0.1%
1802 1
0.1%
1801 1
0.1%
1787 1
0.1%
1782 1
0.1%
1780 2
0.1%
1778 1
0.1%
1769 1
0.1%
1753 1
0.1%
1752 1
0.1%

age_cat
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
952 
1
866 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 952
52.4%
1 866
47.6%

Length

2024-12-19T11:06:18.462639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:18.674282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 952
52.4%
1 866
47.6%

Most occurring characters

ValueCountFrequency (%)
0 952
52.4%
1 866
47.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 952
52.4%
1 866
47.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 952
52.4%
1 866
47.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 952
52.4%
1 866
47.6%

homo_cat
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0
1205 
1
613 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1205
66.3%
1 613
33.7%

Length

2024-12-19T11:06:18.898357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:19.112397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1205
66.3%
1 613
33.7%

Most occurring characters

ValueCountFrequency (%)
0 1205
66.3%
1 613
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1205
66.3%
1 613
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1205
66.3%
1 613
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1205
66.3%
1 613
33.7%

race_cat
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
1289 
0
529 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1818
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1289
70.9%
0 529
29.1%

Length

2024-12-19T11:06:19.396899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T11:06:19.640724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1289
70.9%
0 529
29.1%

Most occurring characters

ValueCountFrequency (%)
1 1289
70.9%
0 529
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1289
70.9%
0 529
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1289
70.9%
0 529
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1289
70.9%
0 529
29.1%

Interactions

2024-12-19T11:05:57.911994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:37.808870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:40.784892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:42.809010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:45.527664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:47.591053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:49.728216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:52.743941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:55.830044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:58.145432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:38.173200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:41.014565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:43.048014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:45.775289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:47.835848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:49.964260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:53.106967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:56.073295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:58.366913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:38.486941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:41.226321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:43.244503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:46.018734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:48.067894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:50.188676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:53.438773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:56.293739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:58.587320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:38.763675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:41.423697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:44.260253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:46.222657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:48.276771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:50.382715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:53.766977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:56.496424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:58.817819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:39.105108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:41.660811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:44.462997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:46.435961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:48.508204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:50.658963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:54.149549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:56.720944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:59.070087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:39.474235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:41.898429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:44.687183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:46.666093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:48.739842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:50.954728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:54.489813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:56.946290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:59.490020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:39.821019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:42.129185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:44.880847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:46.901445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:49.003293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:51.301921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:54.842907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:57.175833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:59.771517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:40.206263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:42.346681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:45.101329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:47.128876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:49.235285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:51.646448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:55.205189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:57.459724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:06:00.033374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:40.564044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:42.595004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:45.311996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:47.359314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:49.476233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:52.389925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:55.581030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T11:05:57.693113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-19T11:06:19.878711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ageage_catcd40cd420cd80cd820ciddrugsgenderhemohomohomo_catkarnofofftrtopriorpidnumpreantiracerace_catstr2stratsymptomtimetreattrtwtkgz30
age1.0000.998-0.032-0.0280.0430.0310.0000.1280.0460.4160.2190.2190.0630.0650.068-0.1330.1230.0940.0940.1240.1090.0660.0800.0000.0000.1570.105
age_cat0.9981.0000.0730.0700.0000.0430.0000.1230.0110.1210.0620.0620.0560.0310.0540.1250.1190.0850.0850.0840.1210.0580.0510.0000.0000.1340.077
cd40-0.0320.0731.0000.6240.2410.0780.2250.0340.0460.0360.0000.0000.0810.1650.0820.037-0.1200.0000.0000.1350.0980.1450.1780.0000.0000.0600.126
cd420-0.0280.0700.6241.0000.0680.2230.3690.0550.0470.0830.0510.0510.0530.1860.122-0.018-0.2090.0360.0360.2340.1600.1040.2670.1120.0780.0560.215
cd800.0430.0000.2410.0681.0000.7160.0530.0000.0930.0900.1260.1260.0000.0360.000-0.0810.0380.0000.0000.0470.0400.0000.0320.0410.0190.0850.058
cd8200.0310.0430.0780.2230.7161.0000.0510.0000.1070.0710.1430.1430.0000.0400.070-0.0840.0240.0000.0000.0000.0000.0000.0290.0700.0540.0720.000
cid0.0000.0000.2250.3690.0530.0511.0000.0310.0290.0000.0300.0300.0700.0720.0270.0220.1260.0480.0480.1170.1230.1360.6270.1040.1020.0800.116
drugs0.1280.1230.0340.0550.0000.0000.0311.0000.1370.0790.2090.2090.0440.1020.0000.1230.0410.0690.0690.0000.0070.0000.0400.0000.0180.0250.000
gender0.0460.0110.0460.0470.0930.1070.0290.1371.0000.1080.6160.6160.0000.0000.0270.1480.0750.3150.3150.0060.0880.0630.0980.0000.0000.3800.024
hemo0.4160.1210.0360.0830.0900.0710.0000.0790.1081.0000.3870.3870.0620.0000.0340.7940.1540.0550.0550.1330.1480.0690.0410.0000.0000.0000.115
homo0.2190.0620.0000.0510.1260.1430.0300.2090.6160.3871.0000.9990.0440.0530.0000.3660.0930.3270.3270.0450.0560.1140.0550.0140.0000.2750.059
homo_cat0.2190.0620.0000.0510.1260.1430.0300.2090.6160.3870.9991.0000.0440.0530.0000.3660.0930.3270.3270.0450.0560.1140.0550.0140.0000.2750.059
karnof0.0630.0560.0810.0530.0000.0000.0700.0440.0000.0620.0440.0441.0000.0800.0630.1170.0590.0000.0000.0750.0680.1000.0000.0000.0060.0000.065
offtrt0.0650.0310.1650.1860.0360.0400.0720.1020.0000.0000.0530.0530.0801.0000.0220.0000.0640.0000.0000.0080.0650.0630.4760.0310.0310.0000.023
oprior0.0680.0540.0820.1220.0000.0700.0270.0000.0270.0340.0000.0000.0630.0221.0000.0730.1460.0000.0000.1220.1320.0000.0000.0200.0000.0000.028
pidnum-0.1330.1250.037-0.018-0.081-0.0840.0220.1230.1480.7940.3660.3660.1170.0000.0731.000-0.0500.1520.1520.2020.1500.104-0.1000.0000.000-0.0570.182
preanti0.1230.119-0.120-0.2090.0380.0240.1260.0410.0750.1540.0930.0930.0590.0640.146-0.0501.0000.1350.1350.8060.8160.0480.1070.0330.022-0.0550.752
race0.0940.0850.0000.0360.0000.0000.0480.0690.3150.0550.3270.3270.0000.0000.0000.1520.1351.0000.9990.0610.1140.0870.0570.0000.0000.1540.056
race_cat0.0940.0850.0000.0360.0000.0000.0480.0690.3150.0550.3270.3270.0000.0000.0000.1520.1350.9991.0000.0610.1140.0870.0570.0000.0000.1540.056
str20.1240.0840.1350.2340.0470.0000.1170.0000.0060.1330.0450.0450.0750.0080.1220.2020.8060.0610.0611.0001.0000.0260.1710.0000.0000.0660.896
strat0.1090.1210.0980.1600.0400.0000.1230.0070.0880.1480.0560.0560.0680.0650.1320.1500.8160.1140.1141.0001.0000.0510.1260.0000.0000.0390.899
symptom0.0660.0580.1450.1040.0000.0000.1360.0000.0630.0690.1140.1140.1000.0630.0000.1040.0480.0870.0870.0260.0511.0000.1210.0000.0000.0230.003
time0.0800.0510.1780.2670.0320.0290.6270.0400.0980.0410.0550.0550.0000.4760.000-0.1000.1070.0570.0570.1710.1260.1211.0000.1400.0750.0300.179
treat0.0000.0000.0000.1120.0410.0700.1040.0000.0000.0000.0140.0140.0000.0310.0200.0000.0330.0000.0000.0000.0000.0000.1401.0000.9990.0260.000
trt0.0000.0000.0000.0780.0190.0540.1020.0180.0000.0000.0000.0000.0060.0310.0000.0000.0220.0000.0000.0000.0000.0000.0750.9991.0000.0000.000
wtkg0.1570.1340.0600.0560.0850.0720.0800.0250.3800.0000.2750.2750.0000.0000.000-0.057-0.0550.1540.1540.0660.0390.0230.0300.0260.0001.0000.056
z300.1050.0770.1260.2150.0580.0000.1160.0000.0240.1150.0590.0590.0650.0230.0280.1820.7520.0560.0560.8960.8990.0030.1790.0000.0000.0561.000

Missing values

2024-12-19T11:06:00.440198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-19T11:06:01.234864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pidnumcidtimetrtagewtkghemohomodrugskarnofopriorz30zpriorpreantiracegenderstr2stratsymptomtreatofftrtcd40cd420cd80cd820age_cathomo_catrace_cat
010056094824889.812800010000100001010422477566324111
3100930116634785.2768010100011139901130102873941590966101
4101240109004366.679201010001113520113000504353870782101
5101400118114688.9056011100011118101130102353398601060101
610165179403173.02960101000119300113000244225708699001
710190095704166.225601110001113290113000401366889720101
810198119834082.55520109001110740113111214107652131101
910229118803578.01920101000119640113001221132221759101
10102411107323495.25600001000118971013010471468770620010
11103410117533876.43160101000114610113010340230660510101
pidnumcidtimetrtagewtkghemohomodrugskarnofopriorz30zpriorpreantiracegenderstr2stratsymptomtreatofftrtcd40cd420cd80cd820age_cathomo_catrace_cat
2126980038188921655.0000100900117911113010198199954811010
21279800391106922466.0000100900117690113010312422352443011
21289800410110132565.0000100900117540113010252241672421011
2129980042158801663.00001001000117530113000299214546471011
21309800450108722578.00001001000119050113010468594636554011
2131980046094812072.40001001000010000101148364117281504011
21339900191104123964.86481009001110420113011378401504367111
21349900210109132153.29801001000118420113011152109561720011
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